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Sensors 2018, 18(9), 2970; https://doi.org/10.3390/s18092970

A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait

1
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
2
Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
*
Author to whom correspondence should be addressed.
Received: 19 July 2018 / Revised: 26 August 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
(This article belongs to the Section Physical Sensors)
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Abstract

Individuals with mobility impairments related to age, injury, or disease, often require the help of an assistive device (AD) such as a cane to ambulate, increase safety, and improve overall stability. Instrumenting these devices has been proposed as a non-invasive way to proactively monitor an individual’s reliance on the AD while also obtaining information about behaviors and changes in gait. A critical first step in the analysis of these data, however, is the accurate processing and segmentation of the sensor data to extract relevant gait information. In this paper, we present a highly accurate multi-sensor-based gait segmentation algorithm that is robust to a variety of walking conditions using an AD. A matched filtering approach based on loading information is used in conjunction with an angular rate reversal and peak detection technique, to identify important gait events. The algorithm is tested over a variety of terrains using a hybrid sensorized cane, capable of measuring loading, mobility, and stability information. The reliability and accuracy of the proposed multi-sensor matched filter (MSMF) algorithm is compared with variations of the commonly employed gyroscope peak detection (GPD) algorithm. Results of an experiment with a group of 30 healthy participants walking over various terrains demonstrated the ability of the proposed segmentation algorithm to reliably and accurately segment gait events. View Full-Text
Keywords: multi-sensor; assistive device; cane; gait analysis; loading information; inertial measurement unit (IMU); stride segmentation multi-sensor; assistive device; cane; gait analysis; loading information; inertial measurement unit (IMU); stride segmentation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Gill, S.; Seth, N.; Scheme, E. A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait. Sensors 2018, 18, 2970.

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